Association between Residential Proximity to Viticultural Areas and Childhood Acute Leukemia Risk in Mainland France: GEOCAP Case-Control Study, 2006-2013.

BACKGROUND: Pesticide exposures are suspected of being a risk factor for several childhood cancers, particularly acute leukemia (AL). Most of the evidence is based on self-reported parental domestic use of pesticides, but some studies have also addressed associations with agricultural use of pesticides near the place of residence. OBJECTIVES: The objective of the study was to evaluate the risk of AL in children living close to vines, a crop subject to intensive pesticide use. METHODS: Data were drawn from the national registry-based GEOCAP study. We included all of the AL cases under the age of 15 years diagnosed in 2006-2013 (n=3,711) and 40,196 contemporary controls representative of the childhood population in France. The proximity of the vines (probability of presence within 200, 500, and 1,000m) and the viticulture density (area devoted to vines within 1,000m) were evaluated around the geocoded addresses in a geographic information system combining three national land use maps. Logistic regression models were used to estimate odds ratios (ORs) for all AL and for the lymphoblastic (ALL) and myeloid (AML) subtypes. Heterogeneity between regions was studied by stratified analyses. Sensitivity analyses were carried out to take into account, in particular, geocoding uncertainty, density of other crops and potential demographic and environmental confounders. RESULTS: In all, about 10% of the controls lived within 1km of vines. While no evidence of association between proximity to vines and AL was found, viticulture density was positively associated with ALL [OR=1.05 (1.00-1.09) for a 10% increase in density], with a statistically significant heterogeneity across regions. No association with AML was observed. The results remained stable in all the sensitivity analyses. CONCLUSION: We evidenced a slight increase in the risk of ALL in children living in areas with high viticulture density. This finding supports the hypothesis that environmental exposure to pesticides may be associated with childhood ALL. https://doi.org/10.1289/EHP12634.

The African swine fever modelling challenge: Model comparison and lessons learnt.

Robust epidemiological knowledge and predictive modelling tools are needed to address challenging objectives, such as: understanding epidemic drivers; forecasting epidemics; and prioritising control measures. Often, multiple modelling approaches can be used during an epidemic to support effective decision making in a timely manner. Modelling challenges contribute to understanding the pros and cons of different approaches and to fostering technical dialogue between modellers. In this paper, we present the results of the first modelling challenge in animal health - the ASF Challenge - which focused on a synthetic epidemic of African swine fever (ASF) on an island. The modelling approaches proposed by five independent international teams were compared. We assessed their ability to predict temporal and spatial epidemic expansion at the interface between domestic pigs and wild boar, and to prioritise a limited number of alternative interventions. We also compared their qualitative and quantitative spatio-temporal predictions over the first two one-month projection phases of the challenge. Top-performing models in predicting the ASF epidemic differed according to the challenge phase, host species, and in predicting spatial or temporal dynamics. Ensemble models built using all team-predictions outperformed any individual model in at least one phase. The ASF Challenge demonstrated that accounting for the interface between livestock and wildlife is key to increasing our effectiveness in controlling emerging animal diseases, and contributed to improving the readiness of the scientific community to face future ASF epidemics. Finally, we discuss the lessons learnt from model comparison to guide decision making.

The African swine fever modelling challenge: Objectives, model description and synthetic data generation.

African swine fever (ASF) is an emerging disease currently spreading at the interface between wild boar and pig farms in Europe and Asia. Current disease control regulations, which involve massive culling with significant economic and animal welfare costs, need to be improved. Modelling enables relevant control measures to be explored, but conducting the exercise during an epidemic is extremely difficult. Modelling challenges enhance modellers' ability to timely advice policy makers, improve their readiness when facing emerging threats, and promote international collaborations. The ASF-Challenge, which ran between August 2020 and January 2021, was the first modelling challenge in animal health. In this paper, we describe the objectives and rules of the challenge. We then demonstrate the mechanistic multi-host model that was used to mimic as accurately as possible an ASF-like epidemic, provide a detailed explanation of the surveillance and intervention strategies that generated the synthetic data, and describe the different management strategies that were assessed by the competing modelling teams. We then outline the different technical steps of the challenge as well as its environment. Finally, we synthesize the lessons we learnt along the way to guide future modelling challenges in animal health.